4 papers · 1 filter
Learning What Not to Learn: Adversarial Disentangled Prompt Tuning for Robust Vision-Language Models
Yang Chen, Zhan Zhuang, Yanbin Wei +3
While adversarial prompt tuning can enhance robustness of vision-language models efficiently, we find that existing methods aggravate robust generalization overfitting on seen clas…
Alternating Training-based Label Smoothing Enhances Prompt Generalization
Yang Chen, Yanbin Wei, Ke Jin +3
Recent advances in pre-trained vision-language models have demonstrated remarkable zero-shot generalization capabilities. To further enhance these models' adaptability to various d…
MoPD: Mixture-of-Prompts Distillation for Vision-Language Models
Yang Chen, Shuai Fu, Yu Zhang
Soft prompt learning methods are effective for adapting vision-language models (VLMs) to downstream tasks. Nevertheless, empirical evidence reveals a tendency of existing methods t…
Think as Cardiac Sonographers: Marrying SAM with Left Ventricular Indicators Measurements According to Clinical Guidelines
Tuo Liu, Qinghan Yang, Yu Zhang +3
Left ventricular (LV) indicator measurements following clinical echocardiog-raphy guidelines are important for diagnosing cardiovascular disease. Alt-hough existing algorithms have…